Fuzzy entropy and its application

Fuzzy logic is based on fuzzy sets. In the classical approach, an element either is or is not the element of the set. On the other hand, in the fuzzy approach, each element has a degree of membership to a set.Fuzzy entropy is used to express the mathematical values of the fuzziness of fuzzy sets. The concept of entropy, the basic subject of information theory and telecommunications, is a measure of fuzziness in fuzzy sets.This study encompasses two applications of fuzzy entropy in the field of image processing, which depend on Shannon?s entropy and distance concept.The first application is the enhancement of the cell count method. It is known that the cell count method of medical doctors requires extreme attention and takes too much time. In addition, it is known that this situation induces count errors due to doctors? workload and leads to loss of time. Consequently, some scientific studies are needed for cell counting. This study aims to use the segmentation method for clear vision in cell counting from histopathological images. The fuzzy entropy method is used in the segmentation process. Before the segmentation process, the images were cleared by removing the noise from the image and a better image for cell count was provided. In the segmentation process, a better threshold value is obtained compared to the previous works, by using generalized fuzzy entropy and Shannon?s entropy. In this study, the results of fuzzy entropy and Shannon?s entropy methods used in cell count application are compared as well.The second application in this study uses the generalized fuzzy entropy method to remove the noise on an image. The noise on the human face image is reduced with the cost function obtained depending on the method of fuzzy entropy. It is aimed to contribute to the studies in the field of health by obtaining better results with this method in the clarification of images particularly such as MR, ECG and ultrasound.